class_docstr: Base class for op benchmarks.
docstr: Runs the op on ragged inputs and on its dense counterpart for comparison.
docstr_tok: ["Runs","the","op","on","ragged","inputs","and","on","its","dense","counterpart","for","comparison","."]
code: def run_and_report_ragged_vs_dense(
      self, fn, iters, burn_iters, xprof_enabled=False, **kwargs
  ):
      """Runs the op on ragged inputs and on its dense counterpart for comparison."""
      ragged_data = self._convert_to_ragged_inputs(self.input_data)
  
      self.input_data = ragged_data
      self.run_and_report(
          fn,
          iters,
          burn_iters,
          xprof_enabled,
          benchmark_name=self._get_method_name() + "_ragged",
          **kwargs,
      )
  
      self.input_data = ragged_data.to_tensor()
      self.run_and_report(
          fn,
          iters,
          burn_iters,
          xprof_enabled,
          benchmark_name=self._get_method_name() + "_dense",
          **kwargs,
      )
  
      self.load_input_data(self.batch_size)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import time
  import tensorflow
  import tensorflow_datasets
  from tensorflow.python.client import session
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow_text.python import ops
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 6 for calling the OpsBaseBenchmark obj's underlying member method code with example usage: obj.run_and_report_ragged_vs_dense(fn, iters, burn_iters, xprof_enabled) without return types